In a background in which network media develops quickly, services such as video on demand, Web television (TV), and video phone already become main services for a broadband network and a wireless network. Operators need to monitor quality of a transmitted video service and take corresponding measures in time to make adjustment to meet experience requirements of users for media services. Quality of network media is affected by many complex factors, including quality of service of a transmission channel (for example, bandwidth, packet loss, delay, and jitter) and matching between parameters of a media coding and decoding end and a transmission channel (for example, coding mode, resolution, error resilience strength, and whether a buffer control policy of a coding and decoding end is appropriate). In addition to packet loss on a channel, coding mode, and the like that cause loss of media data, resulting in degradation of subjective quality of media, channel delay and jitter cause media pause or frame freezing, which also severely affects the subjective quality of media. Therefore, to obtain an accurate model quality score, it is of crucial importance to accurately calculate compression distortion and compression quality that complies with a human eye visual system. It is thus clear that network video quality assessment is an indispensable important technology in network video applications. However, subjective quality assessment by means of observation using human eyes in person is time-consuming and demanding and is obviously infeasible for network video applications.
According to the degree of need for original reference videos, objective video quality assessment methods are classified into three categories: fully referenced, partly referenced, and non-referenced objective video quality assessment. Due to limitation on channel bandwidth, a video receiving end usually cannot obtain a required reference video sequence. Therefore, the non-referenced video quality assessment method needs to be used to assess video streams transmitted in a network.
During application of actual products, algorithm complexity is an issue that needs to be taken into consideration. Real-time monitoring and rating for video data need to be supported on terminal devices with lower computing capabilities (network devices and test devices). Therefore, deep resolution of video streams may not be provided, for example, a specific motion vector (MV) or pixel value cannot be resolved. Another issue that needs to be taken into consideration is that, when a video is encrypted, video content information cannot be acquired. In the prior art, when frame boundary information is not used, calculation by using a packet loss ratio cannot really reflect the degree of video distortion. In addition, usage conditions of an algorithm are limited by many aspects.